Joint Image Reconstruction and Motion Estimation for Spatiotemporal Imaging

Chong Chen (1) , Barbara Gris (2, 3, 4) , Ozan Öktem (5)

1 AMSS - Academy of Mathematics and Systems Science
2 LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
3 CNRS - Centre National de la Recherche Scientifique
4 CaGE - Control And GEometry
5 KTH Royal Institute of Technology - Department of Mathematics

We propose a variational model for joint image reconstruction and motion estimation applicable to spatiotemporal imaging. This model consists of two parts, one that conducts image reconstruction in a static setting and another that estimates the motion by solving a sequence of coupled indirect image registration problems, each formulated within the large deformation diffeomorphic metric mapping framework. The proposed model is compared against alternative approaches (optical flow based model and diffeomorphic motion models). Next, we derive efficient algorithms for a time-discretized setting and show that the optimal solution of the time-discretized formulation is consistent with that of the time-continuous one. The complexity of the algorithm is characterized and we conclude by giving some numerical examples in 2D space + time tomography with very sparse and/or highly noisy data

Mots clés: Spatiotemporal imaging; Image reconstruction; Motion estimation; Joint variational model; Shape theory; Large diffeomorphic deformations

Domaines: Mathématiques [math]

Chong Chen, Barbara Gris, Ozan Öktem. Joint Image Reconstruction and Motion Estimation for Spatiotemporal Imaging. SIAM Journal on Imaging Sciences, 2019. ⟨hal-02386215⟩